Why Your AI Strategy Is Missing the Point
By Faiszal Anwar
Growth Manager & Digital Analyst
The AI industry has spent years obsessing over model performance. GPT-4 vs Claude vs Gemini. Token limits. Reasoning capabilities. We’ve been told that better models equal better results.
But here’s what the model companies won’t tell you: the model is rarely the problem anymore.
At Reltio DataDriven 2026, a clear consensus emerged from data and AI leaders across industries. The next enterprise AI bottleneck isn’t model choice or orchestration. It’s shared context.
The Context Crisis
Think about how your organization actually uses AI today. You have customer data in Salesforce. Transaction history in your data warehouse. Support conversations in Zendesk. Product usage in your analytics platform.
Each system knows something. None of them knows everything.
When you ask an AI to understand your customer, it’s working with fragmented pieces. It doesn’t know that the person who bought Product A last week is the same one who complained on Twitter yesterday. It can’t connect the support ticket from Tuesday to the renewal decision coming next month.
This is the context crisis. Your AI has access to information, but not understanding.
Why This Matters for Growth
Here’s the business reality: context is what makes personalization possible at scale.
Without shared context across systems, your AI can’t truly know your customer. It can guess. It can probabilistically infer. But it can’t act with certainty. And that uncertainty costs you.
Consider the numbers. McKinsey research shows that companies excelling at personalization generate 40% more revenue than average. But that personalization only works when AI has the full picture.
The Fix Isn’t Another Model
The industry is slowly waking up to this. Reltio’s strategic bet is telling: they rebranded their platform around “system-of-context” rather than traditional data management.
The companies winning with AI aren’t chasing the latest model. They’re building the data foundations that make any model powerful.
What does this mean practically? Three things:
First, unify your customer identity. Stop letting customers exist as separate entities across systems. Link them. One profile. Complete view.
Second, make context shareable. Your AI agents need to talk to each other with shared memory. Not just prompt context windows, but persistent understanding of relationships, history, and intent.
Third, prioritize data quality over model quality. A great model with garbage data produces garbage output. Flip your investment ratio.
The Shift That’s Happening
We’re moving from an era of “which model should we use?” to “what context do we need?”
This is harder than just picking an API. It requires data architecture decisions. It demands cross-functional alignment. It means thinking about your data stack as a competitive advantage, not just operational overhead.
But here’s the opportunity: most companies are still stuck on model comparison. If you solve context first, you win regardless of which model dominates next year.
The best part? Context compounds. Every piece of connected data makes your AI smarter. Your competitors can’t just copy your model. They’d have to replicate your entire data ecosystem.
That’s a real moat.
References: